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Proceedings Paper

Multi-sensor image fusion with the steered Hermite transform
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Paper Abstract

The steered Hermite Transform is presented as an efficient tool for multi-sensor image fusion. The fusion algorithm is based on the Hermite transform, which is an image representation model based on Gaussian derivatives that mimic some of the most important properties of human vision. Moreover, rotation of the Hermite coefficients allows efficient detection and reconstruction of oriented image patterns in reconstruction applications such as fusion and noise reduction. We show image fusion with different image sensors, namely synthetic aperture radar (SAR) and multispectral optical images. This case is important mainly because SAR sensors can obtain information independently of weather conditions; however, the characteristic noise (speckle) present in SAR images possesses serious limitations to the fusion process. Therefore noise reduction is a key point in the problem of image fusion. In our case, we combine fusion with speckle reduction in order to discriminate relevant information from noise in the SAR images. The local analysis properties of the Hermite transform help fusion and noise reduction adapt to the local image orientation and content. This is especially useful considering the multiplicative nature of speckle in SAR images.

Paper Details

Date Published: 2 May 2008
PDF: 8 pages
Proc. SPIE 7000, Optical and Digital Image Processing, 70002A (2 May 2008); doi: 10.1117/12.783872
Show Author Affiliations
Boris Escalante-Ramírez, Univ. Nacional Autónoma de México (Mexico)
Alejandra A. López-Caloca, Ctr. de Investigación en Geografía y Geomática (Mexico)


Published in SPIE Proceedings Vol. 7000:
Optical and Digital Image Processing
Peter Schelkens; Touradj Ebrahimi; Gabriel Cristóbal; Frédéric Truchetet, Editor(s)

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